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A retail brand is an asset of value to the stakeholders. Nurturing it enhances quality and stability of earnings. Franchising it across emerging countries provides an opportunity for growth. The challenge, however, is to ensure that the brand stays “connected” with the local markets and “relevant” during changing times. Current research is highly inadequate in guiding the retail brands to stay vibrant when traveling to emerging markets. This research aims to look at how a successful fashion brand in the UK is struggling to grow its retail business through franchise in the Middle East. While the opportunity is vast, the retail brand has been struggling for it has not stayed “relevant”. The paper seeks to present a framework for monitoring its performance so the retail brand can “stay alive”.

Qualitative research was done to understand the profile of the walk‐in customers, their shopping behavior, spending motives, and lifestyles. Quantitative research was done to ascertain how the retail brand was perceived in comparison with other brands in the choice set. Further, the study investigated if the shoppers’ profile and preferences varied across different outlets located in different malls.

By carrying out the analyses, distinct segments were deciphered. It has been noticed that the customer profiles for the retail brand studied varied across different retail locations. Variations in their shopping preferences implied that different merchandising and in‐store promotion activities are required at different outlets to connect with different segments.

The research addresses the gaps in existing literature. The study emphatically confirms that a retail brand franchised cannot take its existence for granted. The study also presents a framework – a dashboard of retail metrics, for measuring, monitoring, evaluating and rejuvenating the performance of retail brands.

Plastic waste is one of the long-standing global issues in the recent era. Unfortunately, India is one of the countries which has been affected by the mismanagement of the use of plastics. India has recorded a substantial growth in the production of plastic and is considered a country of increased consumption of plastic. Due to the absence of an appropriate waste collection and segregation process, it has created the major issue of waste management and discarded used plastic items used for packaging application. There are various plastic waste management laws and programmes that have a cascading effect on almost every sector of business. In 2016, two years after the new union government took power in New Delhi, The Ministry of Environment, Forest and Climate Change (MoEFCC) has made some improvements in rules for the collection, segregation, processing, treatment and disposal of the waste. In the pandemic era, effective plastic waste management became more important than ever. The COVID-19 pandemic has created an increased demand for single-use plastic because of pressure on the already out-of-control global plastic waste problem. It is recorded to be large, and the magnitude of this pandemic related to mismanaged plastic waste is unknown. However, understanding the changing landscape and alarming need for effective plastic waste management, the government of India has proposed certain changes to prohibit imports, handling, manufacturing and use of single-use plastics in the country. This is in line with the government's intent to phase out single-use plastic by 2022. Considering this, this chapter highlights the changes in the rules and regulations in India related to plastic waste management and its effects on various sectors of business.

The purpose of the study is to highlight the effect of human resources information systems (HRISs) implementation success on the job involvement, job satisfaction and work engagement of the employees in small and medium enterprises (SMEs).

Following an investigative study, a quantitative research is done with a positivist approach. A sample of 1,082 responses from 163 SMEs in different economic sectors is analyzed using statistical package for social sciences and structural equation modelling.

The results show that HRIS successful implementation explains the variation in job involvement and work engagement to a limited extent while it does highly affect employees’ job satisfaction. Moreover, the correlation between job involvement and both HRIS implementation success and job satisfaction is negative.

The researchers were not able to conduct intersector analysis for differences among economic sectors. Existing gender differences in job involvement, work engagement and job satisfaction are hinted to and need further analysis.

The HRIS users with higher educational background tend to display a more open approach toward using the system and seeing the system’s implementation succeed. Tenured managers display low enthusiasm toward HRIS success, although being highly involved and engaged in the SME’s life. This provides for not only a steady work-flow but also fosters resistance to change. The successful implementation of the HRIS is a new dimension in the hands of managers at work, facilitating their supervisor work routines and affecting employees’ satisfaction, involvement and engagement.

The HRIS success allows more flexibility at the supervisor level of daily work, opening a window toward flexible working hours and indirect supervision. It allows users some freedom and flexibility and the application of their own ethical beliefs when self-reporting.

Although most research looks at corporations and the factors leading to HRIS implementation success, this study goes beyond the implementation to dwell into Small and Medium Enterprises (SMEs), looking at HRIS after its implementation success, as a potential tool for motivating, involving and engaging employees.

Chatbots emerge as a prominent trend within the context of evolving communication settings and enhancing customer experience to improve firms' total quality management strategies. Specifically, users’ initial trust in such chatbots is critical for their adoption. Under the realm of technology acceptance theories, the present research aims to investigate drivers (perceived ease of use, performance expectancy, compatibility, social influence and technology anxiety) and impacts (customer experience and chatbot usage intention) of chatbot initial trust, among Generation Z considered as the more tech-savvy generation, in the particular telecommunication services context.

Research data were collected using an online questionnaire-based survey to test research hypotheses. A sample of 385 students was selected in Tunisia using a convenience sampling technique. Data were then analyzed through structural equation modeling by AMOS 23.

The results highlighted that, except for perceived ease of use and performance expectancy, all determinants have a significant influence on chatbot initial trust (positive impact of social influence and compatibility and negative impact of technology anxiety). Furthermore, chatbot initial trust positively stimulates customer experience with chatbots and chatbot intention of use.

Our results provide particular insights to chatbot developers seeking to enhance trust-building features in these systems and telecommunication operators to better understand user adoption and improve chatbot-based customer interactions among Generation Z in emergent markets.

This paper attempts to consolidate and enrich the existing body of chatbot initial trust literature by emphasizing the role of customer experience with chatbots and technology anxiety, as two pivotal consumer-related factors that have not yet been treated together in one research.

This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV.

To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically.

In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature.

The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models.

The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection.

The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance.

The authors draw our conclusions based on their own research and experimental works.

This paper aims to study the extent of use of artificial intelligence (AI) in the modern organization; to comprehend the changing nature of future jobs in the context of application of AI; and to study the impact of AI on the economy of the country with special reference to the job market. Given the critical scenario of labor intensive Indian economy, the paper intends to show how AI shall affect rather coexist with human intelligence or labor.

The research on implementation of AI in different industries and its effect on job market are at a nascent stage. There is a dearth of literature. Hence, this study followed a qualitative approach to have a better understanding of the research questions as Bhattacherjee (2012) confirms that employing an interpretive paradigm (qualitative analysis as the analysis of data, e.g. data from interview transcripts) is the more productive way to study social order and that it is achieved through “subjective interpretation of participants involved, such as by interviewing different participants and reconciling differences among their responses using their own subjective perspectives”. Sample selection: The selection technique utilized is purposive sampling. The respondents in this research are the general managers and HRs from different companies. A total of 14 senior professionals from various sectors were approached for the interview out of which seven people gave their consent to take interview. Seven senior HR professionals, mainly general managers and HRs from various sectors viz. oil and gas sector, manufacturing, healthcare, construction, media, power and energy and retail were interviewed to understand how they are using AI in their respective fields. Inclusion Criteria: (1) Generally, the people covered under the research are from the decision-making level of their companies so they are in a position to give strategic perspective as well as day to day implication of implementation of AI. (2) Respondents have adequate knowledge of the respective industry to which they belong. (3) Respondents have reasonable industry of dealing with Human Resource Management and national economy as a whole assessment tool and its administration procedures. A narrative approach was adopted to have a better understanding of the research questions and comprehend their views regarding implementation of AI in their respective companies. A semi structured open ended interview was administered to steer the discussion around the research questions. The respondents were interviewed over the phone and each respondent shared their stories. Analysis of data: The narrations were then transcribed by online transcriber website otter.ai.com. The common keywords as prescribed by the website are as: AI, strategy, learning and implementation. The extracts of the discussions are noted in the next segment of the paper. As and when required this research also used secondary data from the journals, literature available in the websites to understand the implementation of AI globally.

A country where the government itself admits 90% of its workforce belongs to informal sector and conspicuously exits a multi-faceted stark digital divide (Huberman, 2001; DiMaggio et al., 2001; Guillen, 2006; Servon, 2002) wherein gap of digital divide is significant between the rural and urban India (Dasgupta et al., 2002; Nath, 2001; Singh, 2007; Mahajan, 2003; Dutta, 2003) talking of educating, applying and implementing AI seems to be “ a distant dream” but an “ambiguous ambition ”

Prior to implementation of AI that India has to ensure, the basic hygiene factors of informal sector labor force like social security, 2008, low wages and lack of legal protection, unpaid overtime and occupational health problems, poor bargaining power, working without leave under coercion, child care issues and health ailments(for which mere legislation or statutarization is just a formality executed than taking real action) to take the majority of Indian workforce to attain the motivational factor to acquire the knowledge and skill of AI and to implement it.

The AI and its adoption are still at their embryonic stage in Indian companies. With the adoption of such sophisticated technology, in one side, the organizations are dreaming of efficiency, higher productivity and better organizational performance whereas on the other side requirement of changing skill sets and decreasing manpower, creating fear among the mass, which results in hard resistance against the implementation process of AI. On the other hand, lack of expertise and high cost of adoption is also hindering AI to implement in the organizations. The adoption and implementation stage of AI vary from organization to organizations, as well as functions to functions. While the marketing departments of several organizations are using advanced level of AI, there, the HR departments are using AI at the very initial stage. But it is evident from the above discussions that adoption of AI in business functions is inevitable and only it is a matter of time. With the COVID-19 pandemic this has become the utmost necessity for many organizations, particularly who works across the globe. HR partners of the businesses are also adopting AI at a fast pace to do away with the mundane works and deliver efficient services to the stakeholders. It is understood from the discourse that the prerequisite for a successful implementation of AI across the industries throughout the country, needs a concerted effort from industries, academia and government.

The answer lies in Keynesian economics. The central tenet of which is government intervention rather investment to stabilize and progress the economy by way of spreading Internet connectivity, basic literacy and computer literacy, then only truly AI can be effective in a greater scale.

A study on application of artificial intelligence in the pandemic era from a wider perspective, this work is an empirical investigation into the benefits and limitations of artificial intelligence for human potential and labour -intensive pandemic ridden Indian economy.